Methods and systems for arrhythmia tracking and scoring

ABSTRACT

A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional Application No.61/915,113, filed Dec. 12, 2013, which application is incorporatedherein by reference, U.S. Provisional Application No. 61/953,616 filedMar. 14, 2014, U.S. Provisional Application No. 61/969,019, filed Mar.21, 2014, U.S. Provisional Application No. 61/970,551 filed Mar. 26,2014 which application is incorporated herein by reference, and U.S.Provisional Application No. 62/014,516, filed Jun. 19, 2014, whichapplication is incorporated herein by reference.

BACKGROUND

The present disclosure relates to medical devices, systems, and methods.In particular, the present disclosure relates to methods and systems formanaging health and disease such as cardiac diseases includingarrhythmia and atrial fibrillation.

Cardiovascular diseases are the leading cause of death in the world. In2008, 30% of all global death can be attributed to cardiovasculardiseases. It is also estimated that by 2030, over 23 million people willdie from cardiovascular diseases annually. Cardiovascular diseases areprevalent in the populations of high-income and low-income countriesalike.

Arrhythmia is a cardiac condition in which the electrical activity ofthe heart is irregular or is faster (tachycardia) or slower(bradycardia) than normal. Although many arrhythmias are notlife-threatening, some can cause cardiac arrest and even sudden cardiacdeath. Atrial fibrillation is the most common cardiac arrhythmia. Inatrial fibrillation, electrical conduction through the ventricles ofheart is irregular and disorganized. While atrial fibrillation may causeno symptoms, it is often associated with palpitations, shortness ofbreath, fainting, chest, pain or congestive heart failure. Atrialfibrillation is also associated with atrial clot formation, which isassociated with clot migration and stroke.

Atrial fibrillation is typically diagnosed by taking anelectrocardiogram (ECG) of a subject, which shows a characteristicatrial fibrillation waveform

To treat atrial fibrillation, a patient may take medications to slowheart rate or modify the rhythm of the heart. Patients may also takeanticoagulants to prevent atrial clot formation and stroke. Patients mayeven undergo surgical intervention including cardiac ablation to treatatrial fibrillation.

Often, a patient with arrhythmia or atrial fibrillation is monitored forextended periods of time to manage the disease. For example, a patientmay be provided with a Holter monitor or other ambulatoryelectrocardiography device to continuously monitor a patient's heartrate and rhythm for at least 24 hours.

Current ambulatory electrocardiography devices such as Holter monitors,however, are typically bulky and difficult for subjects to administerwithout the aid of a medical professional. For example, the use ofHolter monitors requires a patient to wear a bulky device on their chestand precisely place a plurality of electrode leads on precise locationson their chest. These requirements can impede the activities of thesubject, including their natural movement, bathing, and showering. Oncean ECG is generated, the ECG is sent to the patient's physician who mayanalyze the ECG and provide a diagnosis and other recommendations.Currently, this process often must be performed through hospitaladministrators and health management organizations and many patients donot receive feedback in an expedient manner.

SUMMARY

Disclosed herein are devices, systems, and methods for managing healthand disease such as cardiac diseases, including arrhythmia and atrialfibrillation. In particular, a cardiac disease and/or rhythm managementsystem, according to aspects of the present disclosure, allows a user toconveniently document their electrocardiograms (ECG) and other biometricdata and receive recommendation(s) and/or goal(s) generated by thesystem or by a physician in response to the documented data. The cardiacdisease and/or rhythm management system can be loaded onto a localcomputing device of the user, where biometric data can be convenientlyentered onto the system while the user may continue to use the localcomputing device for other purposes. A local computing device maycomprise, for example, a computing device worn on the body (e.g. ahead-worn computing device such as a Google Glass, a wrist-worncomputing device such as a Samsung Galaxy Gear Smart Watch, etc.), atablet computer (e.g. an Apple iPad, an Apple iPod, a Google Nexustablet, a Samsung Galaxy Tab, a Microsoft Surface, etc.), a smartphone(e.g. an Apple iPhone, a Google Nexus phone, a Samsung Galaxy phone,etc.)

A portable computing device or an accessory thereof may be configured tocontinuously measure one or more physiological signals of a user. Theheart rate of the user may be continuously measured. The continuouslymeasurement may be made with a wrist or arm band or a patch incommunication with the portable computing device. The portable computingdevice may have loaded onto (e.g. onto a non-transitory computerreadable medium of the computing device) and executing thereon (e.g. bya processor of the computing device) an application for one or more ofreceiving the continuously measured physiological signal(s), analyzingthe physiological signal(s), sending the physiological signal(s) to aremote computer for further analysis and storage, and displaying to theuser analysis of the physiological signal(s). The heart rate may bemeasured by one or more electrodes provided on the computing device oraccessory, a motion sensor provided on the computing device oraccessory, or by imaging and lighting sources provided on the computingdevice or accessory. In response to the continuous measurement andrecordation of the heart rate of the user, parameters such as heart rate(HR), heart rate variability (R-R variability or HRV), and heart rateturbulence (HRT) may be determined. These parameters and furtherparameters may be analyzed to detect and/or predict one or more ofatrial fibrillation, tachycardia, bradycardia, bigeminy, trigeminy, orother cardiac conditions. A quantitative heart health score may also begenerated from the determined parameters. One or more of the hearthealth score, detected heart conditions, or recommended user actionitems based on the heart health score may be displayed to the userthrough a display of the portable computing device.

The biometric data may be uploaded onto a remote server where one ormore cardiac technicians or cardiac specialists may analyze thebiometric data and provide ECG interpretations, diagnoses,recommendations such as lifestyle recommendations, and/or goals such aslifestyle goals for subject. These interpretations, diagnoses,recommendations, and/or goals may be provided to the subject through thecardiac disease and/or rhythm management system on their local computingdevice. The cardiac disease and/or rhythm management system may alsoinclude tools for the subject to track their biometric data and theassociated interpretations, diagnoses, recommendations, and/or goalsfrom the cardiac technicians or specialists.

An aspect of the present disclosure includes a dashboard centered aroundarrhythmia or atrial fibrillation tracking. The dashboard includes aheart score that can be calculated in response to data from the usersuch as their ECG and other personal information such as age, gender,height, weight, body fat, disease risks, etc. The main driver of thisheart score will often be the incidence of the user's atrialfibrillation. Other drivers and influencing factors include theaforementioned personal information. The heart score will be frequentlyrelated to output from a machine learning algorithm that combines andweights many if not all of influencing factors.

The dashboard will often display and track many if not all of theinfluencing factors. Some of these influencing factors may be entereddirectly by the user or may be input by the use of other mobile healthmonitoring or sensor devices. The user may also use the dashboard as anatrial fibrillation or arrhythmia management tool to set goals toimprove their heart score.

The dashboard may also be accessed by the user's physician (e.g. thephysician prescribing the system to the user, another regular physician,or other physician) to allow the physician to view the ECG and biometricdata of the user, view the influencing factors of the user, and/orprovide additional ECG interpretations, diagnoses, recommendations,and/or goals.

Another aspect of the present disclosure provides a method for managingcardiac health. Biometric data of a user may be received. A cardiachealth score may be generated in response to the received biometricdata. One or more recommendations or goals for improving the generatedcardiac health score may be displayed to the user. The biometric datamay comprise one or more of an electrocardiogram (ECG), dietaryinformation, stress level, activity level, gender, height, weight, age,body fat percentage, blood pressure, results from imaging scans, bloodchemistry values, or genotype data. The recommendations or goals may beupdated in response to the user meeting the displayed recommendations orgoals. The user may be alerted if one or more recommendations or goalshave not been completed by the user, for example if the user has notcompleted one or more recommendations or goals for the day.

The analysis applied may be through one or more of the generation of aheart health score or the application of one or more machine learningalgorithms. The machine learning algorithms may be trained usingpopulation data of heart rate. The population data may be collected froma plurality of the heart rate monitoring enabled portable computingdevices or accessories provided to a plurality of users. The trainingpopulation of users may have been previously identified as either havingatrial fibrillation or not having atrial fibrillation prior to thegeneration of data for continuously measured heart rate. The data may beused to train the machine learning algorithm to extract one or morefeatures from any continuously measured heart rate data and identifyatrial fibrillation or other conditions therefrom. After the machinelearning algorithm has been trained, the machine learning algorithm mayrecognize atrial fibrillation from the continuously measured heart ratedata of a new user who has not yet been identified as having atrialfibrillation or other heart conditions. One or more of trainingpopulation data or the trained machine learning algorithm may beprovided on a central computing device (e.g. be stored on anon-transitory computer readable medium of a server) which is incommunication with the local computing devices of the users and theapplication executed thereon (e.g. through an Internet or an intranetconnection.)

A set of instructions for managing cardiac health may be downloaded fromthe Internet. These set of instructions may be configured toautomatically generate the cardiac health score. The cardiac healthscore may be generated using a machine learning algorithm. The machinelearning algorithm may generate the cardiac health score of the userand/or the recommendations and/or goals in response to biometric datafrom a plurality of users. The set of instructions may be configured toallow a medical professional to access the received biometric data. Thecardiac health score and/or the recommendations and/or goals may begenerated by the medical professional.

The set of instructions may be stored on a non-transitory computerreadable storage medium of one or more of a body-worn computer, a tabletcomputer, a smartphone, or other computing device. These set ofinstructions may be capable of being executed by the computing device.When executed, the set of instructions may cause the computing device toperform any of the methods described herein, including the method formanaging cardiac health described above.

Another aspect of the present disclosure provides a system for managingcardiac health. The system may comprise a sensor for recording biometricdata of a user and a local computing device receiving the biometric datafrom the sensor. The local computing device may be configured to displaya cardiac health score and one or more recommendations or goals for theuser to improve the cardiac health score in response to the receivedbiometric data.

The system may further comprise a remote server receiving the biometricdata from the local computing device. One or more of the local computingdevice or the remote server may comprise a machine learning algorithmwhich generates one or more of the cardiac health score or the one ormore recommendations or goals for the user. The remote server may beconfigured for access by a medical professional. Alternatively or incombination, one or more of the cardiac health score or one or morerecommendations or goals may be generated by the medical professionaland provided to the local computing device through the remote server.

The sensor may comprise one or more of a hand-held electrocardiogram(ECG) sensor, a wrist-worn activity sensor, a blood pressure monitor, apersonal weighing scale, a body fat percentage sensor, a personalthermometer, a pulse oximeter sensor, or any mobile health monitor orsensor. Often, the sensor is configured to be in wireless communicationwith the local computing device. The local computing device comprisesone or more of a personal computer, a laptop computer, a palmtopcomputer, a tablet computer, a smartphone, a body-worn computer, or thelike. The biometric data may comprise one or more of anelectrocardiogram (ECG), dietary information, stress level, activitylevel, gender, height, weight, age, body fat percentage, or bloodpressure.

Other physiological signals or parameters such as physical activity,heart sounds, blood pressure, blood oxygenation, blood glucose,temperature, activity, breath composition, weight, hydration levels, anelectroencephalograph (EEG), an electromyography (EMG), a mechanomyogram(MMG), an electrooculogram (EOG), etc. may also be monitored. The usermay also input user-related health data such as age, height, weight,body mass index (BMI), diet, sleep levels, rest levels, or stresslevels. One or more of these physiological signals and/or parameters maybe combined with the heart rate data to detect atrial fibrillation orother conditions. The machine learning algorithm may be configured toidentify atrial fibrillation or other conditions in response to heartrate data in combination with one or more of the other physiologicalsignals and/or parameters for instance. Triggers or alerts may beprovided to the user in response to the measured physiological signalsand/or parameters. Such triggers or alerts may notify the user to takecorrective steps to improve their health or monitor other vital signs orphysiological parameters. The application loaded onto and executed onthe portable computing device may provide a health dash boardintegrating and displaying heart rate information, heart healthparameters determined in response to the heart rate information, otherphysiological parameters and trends thereof, and recommended user actionitems or steps to improve health.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the subject matter disclosed herein are set forthwith particularity in the appended claims. A better understanding of thefeatures and advantages of the present disclosure will be obtained byreference to the following detailed description that sets forthillustrative embodiments, in which the principles of the disclosure areutilized, and the accompanying drawings of which:

FIG. 1 shows a system for cardiac disease and rhythm management;

FIG. 2 shows a flow chart of a method 200 for predicting and/ordetecting atrial fibrillation from R-R interval measurements;

FIG. 3 shows a flow chart of a method for predicting and/or detectingatrial fibrillation from R-R interval measurements and for predictingand/or detecting atrial fibrillation from raw heart rate signals;

FIG. 4 shows an embodiment of the system and method of the ECGmonitoring described herein;

FIG. 5 shows a flow chart of an exemplary method to generate a hearthealth score in accordance with many embodiments;

FIG. 6 shows an exemplary method of generating a heart score;

FIG. 7 shows a schematic diagram of the executed application describedherein;

FIG. 8 shows exemplary screenshots of the executed application;

FIG. 9 shows an exemplary method for cardiac disease and rhythmmanagement;

FIG. 10 shows an exemplary method for monitoring a subject to determinewhen to record an electrocardiogram (ECG);

FIG. 11 shows an exemplary screenshot of a first aspect of a dashboardapplication;

FIG. 11A shows an exemplary screenshot of a second aspect of a dashboardapplication;

FIG. 12 shows an exemplary screenshot of a first aspect of a goals andrecommendations page of the cardiac disease and rhythm management systeminterface or mobile app;

FIG. 12A shows an exemplary screenshot of a second aspect of a goals andrecommendations page of the cardiac disease and rhythm management systeminterface or mobile app;

FIG. 13 shows an exemplary screenshot of a user's local computing devicenotifying the user with a pop-up notice to meet their dailyrecommendations and goals; and

FIG. 14 shows an embodiment comprising a smart watch which includes atleast one heart rate monitor and at least one activity monitor.

DETAILED DESCRIPTION

Devices, systems, and methods for managing health and disease such ascardiac diseases, including arrhythmia and atrial fibrillation, aredisclosed. In particular, a cardiac disease and/or rhythm managementsystem, according to aspects of the present disclosure, allows a user toconveniently document their electrocardiograms (ECG) and other biometricdata and receive recommendation(s) and/or goal(s) generated by thesystem or by a physician in response to the documented data.

The term “atrial fibrillation,” denoting a type of cardiac arrhythmia,may also be abbreviated in either the figures or description herein as“AFIB.”

FIG. 1 shows a system 100 for cardiac disease and rhythm management. Thesystem 100 may be prescribed for use by a user or subject such as beingprescribed by the user or subject's regular or other physician ordoctor. The system 100 may comprise a local computing device 101 of theuser or subject. The local computing device 101 may be loaded with auser interface, dashboard, or other sub-system of the cardiac diseaseand rhythm management system 100. For example, the local computingdevice 101 may be loaded with a mobile software application (“mobileapp”) 101 a for interfacing with the system 100. The local computingdevice may comprise a computing device worn on the body (e.g. ahead-worn computing device such as a Google Glass, a wrist-worncomputing device such as a Samsung Galaxy Gear Smart Watch, etc.), atablet computer (e.g. an Apple iPad, an Apple iPod, a Google Nexustablet, a Samsung Galaxy Tab, a Microsoft Surface, etc.), a smartphone(e.g. an Apple iPhone, a Google Nexus phone, a Samsung Galaxy phone,etc.).

The local computing device 101 may be coupled to one or more biometricsensors. For example, the local computing device 101 may be coupled to ahandheld ECG monitor 103. The handheld ECG monitor 103 may be in theform of a smartphone case as described in co-owned U.S. patentapplication Ser. No. 12/796,188 (now U.S. Pat. No. 8,509,882), Ser. Nos.13/107,738, 13/420,520 (now U.S. Pat. No. 8,301,232), Ser. Nos.13/752,048, 13/964,490, 13/969,446, 14/015,303, and 14/076,076, thecontents of which are incorporated herein by reference.

In some embodiments, the handheld ECG monitor 103 may be a handheldsensor coupled to the local computing device 101 with an intermediateprotective case/adapter as described in U.S. Provisional Application No.61/874,806, filed Sep. 6, 2013, the contents of which are incorporatedherein by reference. The handheld ECG monitor 103 may be used by theuser to take an ECG measurement which the handheld ECG monitor 103 maysend to the local computing device by connection 103 a. The connection103 a may comprise a wired or wireless connection (e.g. a WiFiconnection, a Bluetooth connection, a NFC connection, an ultrasoundsignal transmission connection, etc.). The mobile software application101 a may be configured to interface with the one or more biometricsensors including the handheld ECG monitor 103.

The local computing device 101 may be coupled to a wrist-worn biometricsensor 105 through a wired or wireless connection 105 a (e.g. a WiFiconnection, a Bluetooth connection, a NFC connection, an ultrasoundsignal transmission connection, etc.). The wrist-worn biometric sensor105 may comprise an activity monitor such as those available from FitbitInc. of San Francisco, Calif. or a Nike FuelBand available from Nike,Inc. of Oregon. The wrist-worn biometric sensor 105 may also comprise anECG sensor such as that described in co-owned U.S. ProvisionalApplication No. 61/872,555, the contents of which is incorporated hereinby reference.

The local computing device 101 may be coupled to other biometric devicesas well such as a personal scale or a blood pressure monitor 107. Theblood pressure monitor 107 may communicate with the local device 101through a wired or wireless connection 107 a (e.g. a WiFi connection, aBluetooth connection, a NFC connection, an ultrasound signaltransmission connection, etc.).

The local computing device 101 may directly communicate with a remoteserver or cloud-based service 113 through the Internet 111 via a wiredor wireless connection 111 a (e.g. a WiFi connection, a cellular networkconnection, a DSL Internet connection, a cable Internet connection, afiber optic Internet connection, a T1 Internet connection, a T3 Internetconnection, etc.). Alternatively or in combination, the local computingdevice 101 may first couple with another local computing device 109 ofthe user, such as a personal computer of the user, which thencommunicates with the remote server or cloud-based service 113 via awired or wireless connection 109 a (e.g. a WiFi connection, a cellularnetwork connection, a DSL Internet connection, a cable Internetconnection, a fiber optic Internet connection, a T1 Internet connection,a T3 Internet connection, etc.) The local computing device 109 maycomprise software or other interface for managing biometric datacollected by the local computing device 101 or the biometric datadashboard loaded on the local computing device 101.

Other users may access the patient data through the remote server orcloud-based service 113. These other users may include the user'sregular physician, the user's prescribing physician who prescribed thesystem 100 for use by the user, other cardiac technicians, other cardiacspecialists, and system administrators and managers. For example, afirst non-subject user may access the remote server or cloud-basedservice 113 with a personal computer or other computing device 115through an Internet connection 115 a (e.g. a WiFi connection, a cellularnetwork connection, a DSL Internet connection, a cable Internetconnection, a fiber optic Internet connection, a T1 Internet connection,a T3 Internet connection, etc.). Alternatively or in combination, thefirst non-subject user may access the remote server or cloud-basedservice 113 with a local computing device such as a tablet computer orsmartphone 117 through an Internet connection 117 a. The tablet computeror smartphone 117 of the first non-subject user may interface with thepersonal computer 115 through a wired or wireless connection 117 b (e.g.a WiFi connection, a Bluetooth connection, a NFC connection, anultrasound signal transmission connection, etc.). Further, a secondnon-subject user may access the remote server or cloud-based service 113with a personal computer or other computing device 119 through anInternet connection 119 a (e.g. a WiFi connection, a cellular networkconnection, a DSL Internet connection, a cable Internet connection, afiber optic Internet connection, a T1 Internet connection, a T3 Internetconnection, etc.). Further, a third non-subject user may access theremote server or cloud-based service 113 with a tablet computer orsmartphone 121 through an Internet connection 121 a (e.g. a WiFiconnection, a cellular network connection, a DSL Internet connection, acable Internet connection, a fiber optic Internet connection, a T1Internet connection, a T3 Internet connection, etc.). Further, a fourthnon-subject user may access the remote server or cloud-based service 113with a personal computer or other computing device 123 through anInternet connection 123 a (e.g. a WiFi connection, a cellular networkconnection, a DSL Internet connection, a cable Internet connection, afiber optic Internet connection, a T1 Internet connection, a T3 Internetconnection, etc.). The first non-subject user may comprise anadministrator or manager of the system 100. The second non-subject usermay comprise a cardiac technician. The third non-subject user maycomprise a regular or prescribing physician of the user or subject. And,the fourth non-subject user may comprise a cardiac specialist who is notthe user or subject's regular or prescribing physician. Generally, manyif not all of the communication between various devices, computers,servers, and cloud-based services will be secure and HIPAA-compliant.

Aspects of the present disclosure provide systems and methods fordetecting and/or predicting atrial fibrillation or other arrhythmias ofa user by applying one or more machine learning-based algorithms. Aportable computing device (or an accessory usable with the portablecomputing device) may provide R-R intervals and/or raw heart ratesignals as input to an application loaded and executed on the portablecomputing device. The raw heart rate signals may be provided using anelectrocardiogram (ECG) in communication with the portable computingdevice or accessory such as described in U.S. Ser. No. 13/964,490 filedAug. 12, 2013, Ser. No. 13/420,520 filed Mar. 14, 2013, Ser. No.13/108,738 filed May 16, 2011, and Ser. No. 12/796,188 filed Jun. 8,2010. Alternatively or in combination, the raw heart rate signals may beprovided using an on-board heart rate sensor of the portable computingdevice or by using photoplethysmography implemented by an imaging sourceand a light source of the portable computing device. Alternatively or incombination, the raw heart rate signals may be from an accessory deviceworn by the user or attached to the user (e.g. a patch) and which is incommunication with the portable computing device. Such wearableaccessory devices may include Garmin's Vivofit Fitness Band, Fitbit,Polar Heart Rate Monitors, New Balance's Balance Watch, Basis B1 Band,MIO Alpha, Withings Pulse, LifeCORE Heart Rate Monitor strap, and thelike.

R-R intervals may be extracted from the raw heart rate signals. The R-Rintervals may be used to calculate heart rate variability (HRV) whichmay be analyzed in many ways such as using time-domain methods,geometric methods, frequency-domain methods, non-linear methods, longterm correlations, or the like as known in the art. Alternatively or incombination, the R-R intervals may be used for non-traditionalmeasurements such as (i) determining the interval between every other orevery three R-waves to evaluate for bigeminy or trigeminy or (ii) thegeneration of a periodic autoregressive moving average (PARMA).

The machine learning based algorithm(s) may allow softwareapplication(s) to identify patterns and/or features of the R-R intervaldata and/or the raw heart rate signals or data to predict and/or detectatrial fibrillation or other arrhythmias. These extracted and labelledfeatures may be features of HRV as analyzed in the time domain such asSDNN (the standard deviation of NN intervals calculated over a 24 hourperiod), SDANN (the standard deviation of the average NN intervalscalculated over short periods), RMSSD (the square root of the mean ofthe sum of the squares of the successive differences between adjacentNNs), SDSD (the standard deviation of the successive differences betweenadjacent NNs), NN50 (the number of pairs of successive NNs that differby more than 50 ms), pNN50 (the proportion of NN50 divided by totalnumber of NNs), NN20 (the number of pairs of successive NNs that differby more than 20 ms), pNN20 (the proportion of NN20 divided by the totalnumber of NNs), EBC (estimated breath cycle), NNx (the number of pairsof successive NNs that differ by more than x ms), pNNx (the proportionof NNx divided by the number of NNs), or other features known in theart. Alternatively or in combination, the extracted and labelledfeatures may comprise a nonlinear transform of R-R ratio or R-R ratiostatistics with an adaptive weighting factor. Alternatively or incombination, the extracted and labelled features may be features of HRVas analyzed geometrically such as the sample density distribution of NNinterval durations, the sample density distribution of differencesbetween adjacent NN intervals, a Lorenz plot of NN or RR intervals,degree of skew of the density distribution, kurtosis of the densitydistribution, or other features known in the art. Alternatively or incombination, the extracted and labelled features may be features of HRVin the frequency domain such as the power spectral density of differentfrequency bands including a high frequency band (HF, from 0.15 to 0.4Hz), low frequency band (LF, from 0.04 to 0.15 Hz), and the very lowfrequency band (VLF, from 0.0033 to 0.04 Hz), or other frequency domainfeatures as known in the art. Alternatively or in combination, theextracted and labelled features may be non-linear features such as thegeometric shapes of a Poincaré plot, the correlation dimension, thenonlinear predictability, the pointwise correlation dimension, theapproximate entropy, and other features as known in the art. Otherfeatures from the raw heart rate signals and data may also be analyzed.These features include for example a generated autoregressive (AR)model, a ratio of consecutive RR intervals, a normalized ratio ofconsecutive RR intervals, a standard deviation of every 2, 3, or 4 RRintervals, or a recurrence plot of the raw HR signals, among others.

The features of the analysis and/or measurement may be selected,extracted, and labelled to predict atrial fibrillation or otherarrhythmias in real time, e.g. by performing one or more machinelearning operation. Such operations can be selected from among anoperation of ranking the feature(s), classifying the feature(s),labelling the feature(s), predicting the feature(s), and clustering thefeature(s). Alternatively or in combination, the extracted features maybe labelled and saved for offline training of a machine learningalgorithm or set of machine learning operations. For example, theoperations may be selected from any of those above. Any number ofmachine learning algorithms or methods may be trained to identify atrialfibrillation or other conditions such as arrhythmias. These may includethe use of decision tree learning such as with a random forest,association rule learning, artificial neural network, inductive logicprogramming, support vector machines, clustering, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, or the like.

The systems and methods for detecting and/or predicting atrialfibrillation or other conditions such as arrhythmias described hereinmay be implemented as software provided as a set of instructions on anon-transitory computer readable medium. A processor of a computingdevice (e.g. a tablet computer, a smartphone, a smart watch, a smartband, a wearable computing device, or the like) may execute this set ofinstructions to receive the input data and detect and/or predict atrialfibrillation therefrom. The software may be downloaded from an onlineapplication distribution platform such as the Apple iTunes or App Store,Google Play, Amazon App Store, and the like. A display of the computingdevice may notify the user whether atrial fibrillation or otherarrhythmias has been detected and/or if further measurements arerequired (e.g. to perform a more accurate analysis). The software may beloaded on and executed by the portable computing device of the user suchas with the processor of the computing device.

The machine learning-based algorithms or operations for predictingand/or detecting atrial fibrillation or other arrhythmias may beprovided as a service from a remote server which may interact orcommunicate with a client program provided on the computing device ofthe user, e.g. as a mobile app. The interaction or communication may bethrough an Application Program Interface (API). The API may provideaccess to machine learning operations for ranking, clustering,classifying, and predicting from the R-R interval and/or raw heart ratedata, for example.

The machine learning-based algorithms or operations, provided through aremote server and/or on a local application on a local computing device,may operate on, learn from, and make analytical predictions from R-Rinterval data or raw heart rate data, e.g. from a population of users.The R-R interval or raw heart rate data may be provided by the localcomputing device itself or an associated accessory, such as described inU.S. Ser. No. 13/964,490 filed Aug. 12, 2013, Ser. No. 13/420,520 filedMar. 14, 2013, Ser. No. 13/108,738 filed May 16, 2011, and Ser. No.12/796,188 filed Jun. 8, 2010. Thus, atrial fibrillation and otherarrhythmias or other heart conditions can be in a convenient,user-accessible way.

FIG. 2 shows a flow chart of a method 200 for predicting and/ordetecting atrial fibrillation from R-R interval measurements. In a step202, an R-R interval of a user is obtained. In a step 204, the obtainedR-R interval is analyzed using one or more traditional heart ratevariability measurements such as, for example, time domain measures,frequency domain measures, and non-linear heart rate variability. In astep 206, the obtained R-R interval is analyzed using one or morenon-traditional heart rate variability measurements such as, forexample, RR (n-i) for Bigeminy and Trigeminy detection, and thegeneration of a periodic autoregressive moving average (PARMA). In astep 208, a feature selection occurs. In a step 210, a real timeprediction or detection of atrial fibrillation, and/or in a step 212,the heart rate variability measurements may be labelled and saved foroffline training of a machine learning algorithm or set of machinelearning operations, and then may be subsequently used to make a realtime prediction and/or detection of atrial fibrillation.

FIG. 3 shows a flow chart of a method 300 for predicting and/ordetecting atrial fibrillation from R-R interval measurements and forpredicting and/or detecting atrial fibrillation from raw heart ratesignals. In a step 302, raw heart rate signals are obtained from, forexample, an ECG of a user. In a step 304, R-R intervals are obtainedfrom the obtained raw hearth signals. In a step 306, the obtained R-Rinterval is analyzed using one or more traditional heart ratevariability measurements such as, for example, time domain measures,frequency domain measures, and non-linear heart rate variability. In astep 308, the obtained R-R interval is analyzed using one or morenon-traditional heart rate variability measurements such as, forexample, RR (n-i) for bigeminy and trigeminy detection, and thegeneration of a periodic autoregressive moving average (PARMA). In astep 310, features from the obtained heart rate features are analyzedusing one or more of wavelet features and shape based features from aHilbert transform. In a step 312, a feature selection occurs. In a step314, a real time prediction or detection of atrial fibrillation, and/orin a step 316, the heart rate variability measurements may be labelledand saved for offline training of a machine learning algorithm or set ofmachine learning operations, and then may be subsequently used to make areal time prediction and/or detection of atrial fibrillation.

Although the above steps show methods 200 and 300 in accordance withmany embodiments, a person of ordinary skill in the art will recognizemany variations based on the teaching described herein. The steps may becompleted in a different order. Steps may be added or deleted. Some ofthe steps may comprise sub-steps. Many of the steps may be repeated asoften as beneficial to the user or subject.

One or more of the steps of method 200 and 300 may be performed withcircuitry, for example, one or more of a processor or a logic circuitrysuch as a programmable array logic for a field programmable gate array.The circuitry may be programmed to provide one or more of the steps ofmethods 200 and 300, and the program may comprise program instructionsstored on a non-transitory computer readable medium or memory orprogrammed steps of the logic circuitry such as the programmable arraylogic or the field programmable gate array, for example.

Aspects of the present disclosure provide systems and methods formonitoring one or more physiological parameters and providing a triggermessage to the user if the one or more physiological parameter meets apre-determined or learned threshold(s). Two or more of the physiologicalparameters may be combined to provide a trigger message. That is, aparticular trigger message may be provided to the user if two or morepre-determined threshold(s) for the physiological parameter(s) are met.

Table 1 below shows an exemplary table of physiological parameters thatmay be measured (left column), features of interest to be measured orthreshold types to be met (middle column), and exemplary triggermessages (right column).

TABLE 1 Physiological Parameter Measurements/Threshold Sample TriggerMessages Heart Rate Heart Rate Variability (HRV), Non- Measure ECG; SeeYour Doctor linear Transformation of RR Intervals Heart Sound SoundFeatures Abnormal Heart Sound; Measure ECG; See Your Doctor BloodPressure Upper and Lower Thresholds High/Low Blood Pressure; Take BPMedication; Exercise; See Your Doctor Blood Oxygenation O2 Saturation,O2 Saturation High Risk of Hypoventilation; Variability High Risk ofSleep Disorder such as Apnea; See Your Doctor Blood Glucose Upper andLower Thresholds High Risk of Hypoglycemia; See Your Doctor TemperatureTemperature, Temperature Changes Fever; Take OTC Fever Medication; SeeYour Doctor Physical Activity Gait, Chest Compressions, Speed, MonitorSenior or Infant Posture, e.g. if (accelerometer data) Distancesenior/infant has fallen Electrocardiogram ECG Features (E.g. QT, QRS,PR High Risk of Certain Cardiac Diseases; (ECG) intervals, HRV, etc.Sleep apnea; See Your Doctor Breath Content Percentage of the CertainChemicals High Risk of Certain Dental Disease, (Breathalyzer data)Diabetes, etc.; See Your Doctor

The machine learning based algorithms or operations as described hereinmay be used to determine the appropriate trigger thresholds in responseto the raw physiological data input and/or user-input physiologicalparameters (e.g. age, height, weight, gender, etc.). Features of the rawphysiological data input may be selected, extracted, labelled,clustered, and/or analyzed. These processed features may then beanalyzed using one or more machine learning operation such as rankingthe feature(s), classifying the feature(s), predicting the feature(s),and clustering the feature(s). The various machine learning algorithmsdescribed herein may be used to analyze the features to detect andpredict health conditions and generate recommendations or user actionitems to improve the health of the user. For instance, the machinelearning algorithms may be trained to identify atrial fibrillation orother conditions in response to the non-heart rate physiologicalparameter(s) such as age, gender, body mass index (BMI), activity level,diet, and others in combination with the raw heart rate data and HRVthat can be extracted therefrom.

The systems and methods for monitoring one or more physiologicalparameters and providing a trigger message to the user if the one ormore physiological parameter meets a pre-determined threshold(s)described herein may be implemented as software provided as a set ofinstructions on a non-transitory computer readable medium. A processorof a computing device (e.g. a tablet computer, a smartphone, a smartwatch, a smart band, a wearable computing device, or the like) mayexecute this set of instructions to receive the input data and detectand/or predict atrial fibrillation therefrom. The software may bedownloaded from an online application distribution platform such as theApple iTunes or App Store, Google Play, Amazon App Store, and the like.The software may be loaded on and executed by the portable computingdevice of the user such as with the processor of the computing device.The software may also provide both the triggering application describedherein and the heart rate monitoring and analysis for detecting atrialfibrillation or other heart conditions described herein.

In an embodiment, a method and system for longitudinal monitoring of apatient's or any consumer's (after referred to as “patient”) healthusing various ECG monitoring devices is described herein. The ECGmonitoring devices generate ECG signal data which can be stored in adatabase for further analysis. The ECG data, which can be stored in adatabase along with other patient information, can be analyzed by aprocessing device, such as a computer or server, using variousalgorithms.

Various ECG monitoring or recording devices, hereinafter referred to asECG monitoring devices, can be used to record the ECG data. For example,the ECG monitoring device can be a handheld, portable, or wearablesmartphone based device, as described in U.S. Pat. No. 8,301,232, whichis herein incorporated by reference in its entirety for all purposes. Asmartphone based device, or a device having wireless or cellulartelecommunication capabilities, can transmit the ECG data to a databaseor server directly through the internet. These types of ECG monitoringdevices as well as other ECG monitoring devices include portabledevices, wearable recording devices, event recorders, and Holtermonitors. Clinical or hospital based ECG recording devices can also beused and integrated into the system. Such devices may be able totransmit stored ECG data through a phone line or wirelessly through theinternet or cellular network, or may need to be sent to a datacollection center for data collection and processing. The ECG data canbe tagged with the type of ECG monitoring device used to record the databy, for example, including it in metadata for indexing and searchingpurposes.

The ECG monitoring devices can be single lead devices or multiple leaddevices, where each lead generally terminates with an electrode. Someembodiments may even be leadless and have electrodes that are integratedwith the body or housing of the device, and therefore have apredetermined relationship with each other, such as a fixed spacingapart from each other. The orientation and positioning of the singlelead in a single lead device or of each lead of the multiple lead deviceor of the electrodes of the leadless device can be transmitted with theECG data. The lead and/or electrode placement may be predetermined andspecified to the patient in instructions for using the device. Forexample, the patient may be instructed to position the leads and/orelectrodes with references to one or more anatomical landmarks on thepatient's torso. Any deviation from the predetermined lead and/orelectrode placement can be notated by the patient or user whentransmitting the ECG data. The lead and electrode placement may beimaged using a digital camera, which may be integrated with a smartphone, and transmitted with the ECG data and stored in the database. Thelead and electrode placement may be marked on the patient's skin forimaging and for assisting subsequent placement of the leads andelectrodes. The electrodes can be attached to the skin usingconventional methods which may include adhesives and conducting gels, orthe electrodes may simply be pressed into contact with the patient'sskin. The lead and electrode placement may be changed after taking onerecording or after recording for a predetermined or variable amount oftime. The ECG data can be tagged with the numbers of leads and/orelectrodes and the lead and/or electrode placement, including whetheradhesives and/or conducting gels were used. Again, this information canbe including in metadata for indexing and searching purposes.

The ECG signal data can be continuously recorded over a predetermined orvariable length of time. Continuous ECG recording devices can record forup to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 days.Alternatively or additionally, the ECG data can be recorded on demand bythe patient at various discrete times, such as when the patient feelschest pains or experiences other unusual or abnormal feelings. The ondemand ECG recorder can have a memory buffer that can record apredetermined amount of ECG data on a rolling basis, and when activatedby the patient to record a potential event, a predetermined amount ofECG data can be saved and/or transmitted. The predetermined amount ofECG data can include a predetermined amount of ECG data beforeactivation and a predetermined amount of ECG data after activation suchthat a window of ECG data is captured that encompasses the potentialevent. The time period between ECG recordings may be regular orirregular. For example, the time period may be once a day, once a week,once a month, or at some other predetermined interval. The ECGrecordings may be taken at the same or different times of days, undersimilar or different circumstances, as described herein. One or morebaseline ECGs can be recorded while the patient is free of symptoms. Thebaseline ECGs can be periodically recorded and predetermined intervalsand/or on-demand. The same ECG recording device or different ECGrecording devices may be used to record the various ECG of a particularpatient. All this information may be tagged to or associated with theECG data by, for example, including it in the metadata for indexing andsearching purposes.

The ECG data can be time stamped and can be annotated by the patient orhealth care provider to describe the circumstances during which the ECGwas recorded, preceding the ECG recording, and/or following the ECGrecording. For example, the system and device can have an user interfacefor data entry that allows the patient to enter in notes regarding theconditions and circumstances surrounding the ECG recording. Thisadditional data can be also included as metadata for indexing andsearching purposes. For example, location, food, drink, medicationand/or drug consumption, exercise, rest, sleep, feelings of stress,anxiety, pain or other unusual or abnormal feelings, or any othercircumstance that may affect the patient's ECG signal can all beinputted into the device, smart phone, computer or other computingdevice to be transmitted to the server or database along with the ECGdata. The annotated data can also include the patient's identity orunique identifier as well as various patient characteristics includingage, sex, race, ethnicity, and relevant medical history. The annotateddata can also be time stamped or tagged so that the ECG data can bematched or correlated with the activity or circumstance of interest.This also allows comparison of the ECG before, after and during theactivity or circumstance so that the effect on the ECG can bedetermined.

The ECG data and the associated metadata can be transmitted from thedevice to a server and database for storage and analysis. Thetransmission can be real-time, at regular intervals such as hourly,daily, weekly and any interval in between, or can be on demand. Themetadata facilitates the searching, organizing, analyzing and retrievingof ECG data. Comparison and analysis of a single patient's ECG data canbe performed, and/or comparison of ECG data between patients can beperformed. For example, the metadata can be used to identify and selecta subset of ECG data where an activity or circumstance, such as thetaking of medication, occurred within a predetermined amount of time tothe ECG data. The components of the ECG signal data, such as the P wave,T wave, and QRS complex and the like, the amplitudes of the components,the ratios between the components, the width of the components, and thedelay or time separation between the components, can be extracted,compared, analyzed, and stored as ECG features. For example, the P waveand heart rate can be extracted and analyzed to identify atrialfibrillation, where the absence of P waves and/or an irregular heartrate may indicate atrial fibrillation. The extracted ECG features canalso be included in the metadata for indexing and searching.

The changes in the ECG signal over time in view of the activities andcircumstances can be compared with changes over time and circumstancesobserved within a database of ECG's. Comparisons may include anycomparison of data derived from any other ECG signal or any database ofECG's or any subset of ECG data, or with data derived from any databaseof ECG's. Changes in any feature of the ECG signal over time may be usedfor a relative comparison with similar changes in any ECG database orwith data derived from an ECG database. The ECG data from the baselineECG and the ECG data from a potential adverse event can be compared todetermine the changes or deviations from baseline values. In addition,both the baseline ECG and the ECG data recorded from the patient can becompared to one or more predetermined template ECGs which can representa normal healthy condition as well as various diseased conditions, suchas myocardial infarction and arrhythmias.

The comparisons and analysis described herein can be used to drawconclusions and insights into the patient's health status, whichincludes potential health issues that the patient may be experiencing atthe time of measurement or at future times. Conclusions anddeterminations may be predictive of future health conditions ordiagnostic of conditions that the patient already has. The conclusionsand determinations may also include insights into the effectiveness orrisks associated with drugs or medications that the patient may betaking, have taken or may be contemplating taking in the future. Inaddition, the comparisons and analysis can be used to determinebehaviors and activities that may reduce or increase risk of an adverseevent. Based on the comparisons and analysis described herein, the ECGdata can be classified according to a level of risk of being an adverseevent. For example, the ECG data can be classified as normal, low risk,moderate risk, high risk, and/or abnormal. The normal and abnormaldesignation may require health care professional evaluation, diagnosis,and/or confirmation.

Diagnosis and determination of an abnormality, an adverse event, or adisease state by physicians and other health care professionals can betransmitted to the servers and database to be tagged with and associatedwith the corresponding ECG data. The diagnosis and determination may bebased on analysis of ECG data or may be determined using other tests orexamination procedures. Professional diagnosis and determinations can beextracted from the patient's electronic health records, can be enteredinto the system by the patient, or can be entered into the system by themedical professional. The conclusions and determinations of the systemcan be compared with actual diagnosis and determinations from medicalprofessions to validate and/or refine the machine learning algorithmsused by the system. The time of occurrence and duration of theabnormality, adverse event or disease state can also be included in thedatabase, such that the ECG data corresponding with the occurrenceand/or the ECG data preceding and/or following the abnormality, adverseevent or disease state can be associated together and analyzed. Thelength of time preceding or following the abnormality may bepredetermined and be up to 1 to 30 days, or greater than 1 to 12 months.Analysis of the time before the abnormality, adverse event or diseasestate may allow the system to identify patterns or correlations ofvarious ECG features that precede the occurrence of the abnormality,adverse event or disease state, thereby providing advance detection orwarning of the abnormality, adverse event or disease state. Analysis ofthe time following the abnormality, adverse event or disease state canprovide information regarding the efficacy of treatments and/or providethe patient or physician information regarding disease progression, suchas whether the patient's condition in improving, worsening or stayingthe same. The diagnosis and determination can also be used for indexingby, for example, including it in the metadata associated with thecorresponding ECG data.

As described herein, various parameters may be included in the databasealong with the ECG data. These may include the patient's age, gender,weight, blood pressure, medications, behaviors, habits, activities, foodconsumption, drink consumption, drugs, medical history and other factorsthat may influence a patient's ECG signal. The additional parameters mayor may not be used in the comparison of the changes in ECG signal overtime and circumstances.

The conclusions, determinations, and/or insights into the patient'shealth generated by the system may be communicated to the patientdirectly or via the patient's caregiver (doctor or other healthcareprofessional). For example, the patient can be sent an email or textmessage that is automatically generated by the system. The email or textmessage can be a notification which directs the patient to log onto asecure site to retrieve the full conclusion, determination or insight,or the email or text message can include the conclusion, determinationor insight. Alternatively or additionally, the email or text message canbe sent to the patient's caregiver. The notification may also beprovided via an application on a smartphone, tablet, laptop, desktop orother computing device.

As described herein, the system can identify behaviors, habits,activities, foods, drinks, medications, drugs, and the like which areassociated with the patient's abnormal ECG readings. In addition toinforming the patient of these associations, the system can provideinstructions or recommendations to the patient to avoid these behaviors,habits, activities, foods, drinks, medications, drugs, and the likewhich are associated with the patient's abnormal ECG readings.Similarly, the system can identify behaviors, habits, activities, foods,drinks, medications, drugs, and the like which are associated withnormal or improving ECG readings, and can instruct or recommend that thepatient perform these behaviors, habits, and activities and/or consumethese foods, drinks, medications, and drugs. The patient may avoid afuture healthcare issue, as instructed or recommended by the system, bymodifying their behavior, habits or by taking any course of action,including but not limited to taking a medication, drug or adhering to adiet or exercise program, which may be a predetermined course of actionrecommended by the system independent of any analysis of the ECG data,and/or may also result from insights learned through this system andmethod as described herein. In addition, the insights of the system mayrelate to general fitness and or mental wellbeing.

The ECG data and the associated metadata and other related data asdescribed herein can be stored in a central database, a cloud database,or a combination of the two. The data can be indexed, searched, and/orsorted according to any of the features, parameters, or criteriadescribed herein. The system can analyze the ECG data of a singlepatient, and it can also analyze the ECG data of a group of patients,which can be selected according to any of the features, parameters orcriteria described herein. When analyzing data from a single patient, itmay be desirable to reduce and/or correct for the intra-individualvariability of the ECG data, so that comparison of one set of ECG datataken at one particular time with another set of ECG data taken atanother time reveals differences resulting from changes in health statusand not from changes in the type of ECG recording device used, changesin lead and electrode placement, changes in the condition of the skin(i.e. dry, sweaty, conductive gel applied or not applied), and the like.As described above, consistent lead and electrode placement can helpreduce variability in the ECG readings. The system can also retrieve thepatient's ECG data that were taken under similar circumstances and cananalyze this subset of ECG data.

FIG. 4 illustrates an embodiment of the system and method 400 of ECGmonitoring described herein. The system can be implemented on a serveror computer having a processor for executing the instructions describedherein, which can be stored in memory. In step 402, ECG data can berecorded using any of the devices described herein for one or morepatients. In step 404, the ECG data is transmitted along with associatedmetadata to a server and database that stores the ECG data. In step 406,a subset of the ECG data can be selected based on criteria in themetadata, such as user identity, time, device used to record the ECGdata, and the like. In step 408, the subset of ECG data can be analyzedusing a machine learning algorithm, which can assign a risk level to theECG data in step 410. The system can then determine whether the risklevel is high, as shown in step 412. If the risk level is low, the usercan be notified that the ECG is normal or low risk, as shown in step414. If the risk level is high, a high risk level alert can be sent tothe patient with the option of sending the ECG to the medicalprofessional for interpretation, as shown in step 416. The system thenwaits for the user's response to determine whether the patient elects tosend the ECG to the medical professional for interpretation, as shown instep 418. If the patient does not wish to send the ECG to the medicalprofessional for interpretation, the system can end the routine at thispoint, as shown in 420. If the patient does elect to send the ECG to themedical professional for interpretation, the request can be transmittedto the medical professional in step 422. The request to the medicalprofessional can be sent to a workflow auction system as described inU.S. Provisional Application No. 61/800,879, filed Mar. 15, 2013, whichis herein incorporated by reference in its entirety for all purposes.Once the medical professional has interpreted the ECG, the system canreceive and store the ECG interpretation from the medical professionalin the database, as shown in step 424. The system can then notify theuser of the professional ECG interpretation, which can be sent to oraccessed by the user, as shown in step 426. Additionally, the system cancompare the assigned risk level with the medical diagnosis in step 428and can determine whether the risk level determined by the system agreeswith the medical diagnosis in step 430. If the risk level does not agreewith the medical diagnosis, the machine learning algorithm can beadjusted until the risk level matches the medical diagnosis, as shown instep 432. If the risk level does agree with the medical diagnosis, theroutine can be ended as shown in step 434.

Although the above steps show a method 400 in accordance with manyembodiments, a person of ordinary skill in the art will recognize manyvariations based on the teaching described herein. The steps may becompleted in a different order. Steps may be added or deleted. Some ofthe steps may comprise sub-steps. Many of the steps may be repeated asoften as beneficial to the user or subject.

One or more of the steps of a method 400 may be performed withcircuitry, for example, one or more of a processor or a logic circuitrysuch as a programmable array logic for a field programmable gate array.The circuitry may be programmed to provide one or more of the steps of amethod 400, and the program may comprise program instructions stored ona non-transitory computer readable medium or memory or programmed stepsof the logic circuitry such as the programmable array logic or the fieldprogrammable gate array, for example.

Aspects of the present disclosure provide systems and methods forgenerating a heart health score in response to continuously measured ormonitored physiological parameter(s). The score may be given aquantitative value such as be graded from A to F or 0 to 100 for example(e.g. a great score may be an A or 100, a good score may be a B or 75, amoderate score may be a C or 50, a poor score may be a D or 25, and afailing score may be an F or 0.) If an arrhythmia is detected, the scoremay be below 50 for example. Other scoring ranges such as A to Z, 1 to5, 1 to 10, 1 to 1000, etc. may also be used. Arrhythmia may bedetecting using the machine learning based operations or algorithmsdescribed herein.

FIG. 5 shows a flow chart of an exemplary method 500 to generate a hearthealth score in accordance with many embodiments.

In a step 502, an arrhythmia is detected. If an arrhythmia is detected(e.g. using the methods and/or algorithms disclosed herein), then theheart health score generated will be below 50. Depending on the severityof the arrhythmia detected, the heart score may be calculated orassigned within the ranges according to the table below in Table 2.

TABLE 2 Arrhythmia Heart Health score ATRIAL FIBRILLATION, HR below 10030-45 ATRIAL FIBRILLATION, HR above 100 15-30 Sinus Tachycardia 20-40Supraventricular Tachycardia 20-40 Bradycardia 20-40 Bigeminy, Trigeminy30-50 Short runs of High Heart Rate (VTACH suspect) 10-30

In a step 504 a Heart Rate Variability (HRV) is calculated. HRV can bean indicator of heart health. The value for HRV value for a healthyheart is typically higher than HRV for an unhealthy heart. Also, HRVtypically declines with age and may be affected by other factors, likestress, lack of physical activity, etc. HRV may be measured and analyzedusing the methods described above. HRV may be calculated in the absenceof arrhythmia, which may improve the accuracy of the HRV measurement.HRV may be determined and further analyzed as described above.

In a step 506, premature beats are counted and Heart Rate Turbulence(HRT) is calculated. Premature beats in the sequence of R-R intervalsmay be detected. Also, R-R intervals typically tend to recover at acertain pace after a premature beat. Using these two parameters(prematurity and pace of R-R recovery), HRT parameters may becalculated. There may be known deviations of HRT parameters associatedwith patients with risk of Congestive Heart Failure (CHF). Thesedeviations, however, may be used to estimate an inverse measure. Thenumber of premature beats per day (or per hour) may also be used as ameasure of heart health. A low number of premature beats may indicatebetter heart health. In summary, the heart health score may be generatedby combining at least heart rate variability (HRV), the number ofpremature beats, and heart rate turbulence (HRT). This combination (inthe absence of arrhythmia) may provide an accurate estimate of howhealthy the heart of the user is.

In a step 508, a heart health score is generated, and in a step 510, ahearth health score is generated based on an arrhythmia. To initiallygenerate the score, a few hours (e.g. 2-5 hours) of measured R-Rintervals may be required. A more accurate score may be generated aftera week of continuous R-R interval measurements. Longer data sets may berequired to detect significant arrhythmias as they may usually bedetected within the first 7-8 days of monitoring.

Although the above steps show a method 500 in accordance with manyembodiments, a person of ordinary skill in the art will recognize manyvariations based on the teaching described herein. The steps may becompleted in a different order. Steps may be added or deleted. Some ofthe steps may comprise sub-steps. Many of the steps may be repeated asoften as beneficial to the user or subject.

One or more of the steps of a method 500 may be performed withcircuitry, for example, one or more of a processor or a logic circuitrysuch as a programmable array logic for a field programmable gate array.The circuitry may be programmed to provide one or more of the steps of amethod 500, and the program may comprise program instructions stored ona non-transitory computer readable medium or memory or programmed stepsof the logic circuitry such as the programmable array logic or the fieldprogrammable gate array, for example.

FIG. 6 shows a further method 600 of generating a heart score. Inaddition to the parameters which may be derived from the heart rate datadescribed above, the heart health score may also be generated inresponse to further physiological parameters as shown in FIG. 6.

In a step 602, a raw ECG waveform is obtained. In a step 608, ECGparameters are extracted from the raw ECG waveform data and arrhythmiaprediction and/or detection algorithms are run to analyze the obtainedraw ECG waveform data.

In a step 604, physiological parameters may be measured using a sensorof the user's local computing device or an accessory thereof. Suchmeasured physiological parameters may include blood pressure, useractivity and exercise level, blood oxygenation levels, blood sugarlevels, an electrocardiogram, skin hydration or the like of the user.These physiological parameters may be measured over time such as oversubstantially the same time scale or length as the measurement of heartrate. In a step 610, an R-R interval is extracted and both traditionaland non-traditional heart rate measures are used to analyze the measuredheart rate and physiological parameters.

In a step 606, additional physiological parameters for determining theheart health score may be input by the user. These parameters mayinclude the age, the gender, the weight, the height, the body type, thebody mass index (BMI), the personal medical history, the family medicalhistory, the exercise and activity level, the diet, the hydration level,the amount of sleep, the cholesterol level, the alcohol intake level,the caffeine intake level, the smoking status, and the like of the user.For example, the heart health score may be weighted by age and/or genderto provide the user an accurate assessment of his or her heart health inresponse to the heart rate data. In a step 612, feature extraction isused to analyze the inputted physiological parameters.

In a step 614 feature ranking and/or feature selection occurs. In a step618, a real time prediction or detection of atrial fibrillation, and/orin a step 616, the heart rate variability measurements may be labelledand saved for offline training of a machine learning algorithm or set ofmachine learning operations, and then may be subsequently used to make areal time prediction and/or detection of atrial fibrillation. Aplurality of heart health scores may be generated by a plurality ofusers to generate a set of population data. This population data may beused to train the machine learning algorithms described herein such thatthe trained algorithm may be able to detect and predict atrialfibrillation or other health conditions from user data.

Although the above steps show a method 600 in accordance with manyembodiments, a person of ordinary skill in the art will recognize manyvariations based on the teaching described herein. The steps may becompleted in a different order. Steps may be added or deleted. Some ofthe steps may comprise sub-steps. Many of the steps may be repeated asoften as beneficial to the user or subject.

One or more of the steps of a method 600 may be performed withcircuitry, for example, one or more of a processor or a logic circuitrysuch as a programmable array logic for a field programmable gate array.The circuitry may be programmed to provide one or more of the steps of amethod 600, and the program may comprise program instructions stored ona non-transitory computer readable medium or memory or programmed stepsof the logic circuitry such as the programmable array logic or the fieldprogrammable gate array, for example.

The systems and methods for generating a heart health score in responseto continuously measured or monitored physiological parameter(s) maycomprise a processor of a computing device and software. A processor ofa computing device (e.g. a tablet computer, a smartphone, a smart watch,a smart band, a wearable computing device, or the like) may execute thisset of instructions to receive the input data and detect and/or predictatrial fibrillation therefrom. The software may be downloaded from anonline application distribution platform such as the Apple iTunes or AppStore, Google Play, Amazon App Store, and the like. A display of thecomputing device may notify the user of the calculated heart healthscore and/or if further measurements are required (e.g. to perform amore accurate analysis).

FIG. 7 shows a schematic diagram of the executed application describedherein. The heart health score may be provided on a software applicationsuch as a mobile app downloaded from an application distributionplatform and executed on a local computing device of the user asdescribed above. This executed application may instruct the user to takeactive steps in response to a poor or moderate heart health score. Forexample, the instructions to the user may be to make a correctivemeasure such as to modify his or her diet, exercise pattern, sleeppattern, or the like. Alternatively or in combination, the instructionsto the user may be to take a further step such as to take anelectrocardiogram (e.g. to verify the presence of an arrhythmia), enrollin an electrocardiogram over-read service, or schedule an appointmentwith a physician or other medical specialist. If the heart health scoreis below a desired threshold for good heart health, the executedapplication may link the user to a second execute application withfurther application features. Alternatively or in combination, thesefurther features may be unlocked on the first executed application ifthe heart health score is below the threshold. In at least some cases, aprescription or verification from a medical professional may also berequired to unlock the further application features.

FIG. 8 shows screenshots of the executed application. The furtherfeatures unlocked may include the ability to read electrocardiogram(ECG) data from a sensor coupled to the local computing device anddisplay the electrocardiogram (ECG) in real-time and/or detect and alertfor atrial fibrillation based on the electrocardiogram (ECG) inreal-time (e.g. as described in U.S. application Ser. Nos. 12/796,188,13/108,738, 13/420,540, and 13/964,490). As shown in FIG. 8, thesefurther features may include an electrocardiogram (ECG) over-readservice such as that described in U.S. application Ser. No. 14/217,032.The first executed application may comprise a consumer softwareapplication and the second executed application may comprise a medicalprofessional or regulated software application or set of features of thefirst executed application. As described herein and shown in FIG. 8, theexecuted application may provide a dash board to track the heart healthof the user and show risk factors which may be monitored and tracked bythe user. The dash board may be provided with further features such asthat described in U.S. Ser. No. 61/915,113 (filed Dec. 12, 2013).

FIG. 9 shows a method 900 for cardiac disease and rhythm management,which may, for example, be implemented with the system 100 describedherein. In a step 902, a user or subject is provided access to a cardiacdisease and/or rhythm management system such as system 100. Step 902 maycomprise prescribing the use of the system 100 for the user or subject.In a step 904, the user or subject is provided one or more biometricsensors. These biometric sensor(s) may couple to a computing device ofthe user or subject, e.g. a personal desktop computer, a laptopcomputer, a tablet computer, a smartphone, etc., and associated softwareloaded thereon.

In a step 906, the user or subject downloads the cardiac disease and/orrhythm management system software onto their computing device. Forexample, the system software may comprise a mobile software application(“mobile app”) downloaded from the Apple App Store, Google Play, AmazonAppstore, BlackBerry World, Nokia Store, Windows Store, Windows PhoneStore, Samsung Apps Store, and the like. The downloaded system software,e.g. mobile app 101 a, may be configured to interface with the biometricsensors provided to the user or subject in the step 154.

In a step 908, personal information input to the cardiac diseasemanagement system is received. For example, the user or subject mayenter his or her gender, height, weight, diet, disease risk factors,etc. into the mobile app 101 a. Alternatively or in combination, thispersonal information may be input on behalf of the user or subject, forexample, by a physician of the user or subject.

In a step 910, biometric data is received from the biometric sensorsprovided to the user or subject. For example, the system 100 and themobile app 101 a may receive ECG data and heart rate from handheldsensor 103, activity data from wrist-worn activity sensor 105, bloodpressure and heart rate data from mobile blood pressure monitor 107 a,and other data such as weight and body fat percentage data from a“smart” scale in communication with the local computing device 101.

In a step 912, a cardiac health score is generated. The cardiac healthscore can be generated by considering and weighing one or moreinfluencing factors including the incidence of atrial fibrillation orarrhythmia as detected by the handheld ECG monitor, the heart rate ofthe user or subject, the activity of the user or subject, hours of sleepand rest of the user or subject, blood pressure of the user or subject,etc. Often, the incidence of atrial fibrillation or arrhythmia will beweighed the most. The cardiac health score may be generated by aphysician or a machine learning algorithm provided by the remote serveror cloud-based service 113, for example. A plurality of users andsubject may concurrently use the cardiac health and/or rhythm managementsystem 100 and the machine learning algorithm may, for example, considerpopulation data and trends to generate an individual user or subject'scardiac health score.

In a step 914, one or more recommendations or goals is generated for theuser or subject based on or in response to the generated cardiac healthscore. These recommendation(s) and/or goal(s) may be generatedautomatically based on or in response to the biometric and personalinformation of the user or subject. For example, the machine learningalgorithm may generate these recommendation(s)/goal(s). Alternatively orin combination, a physician or other medical specialist may generate therecommendation(s) and/or goal(s), for example, based on or in responseto the biometric and personal information of the user or subject. Thephysician or other medical professional may access the patient datathrough the Internet as described above.

In a step 916, the patient implements many if not all of therecommendation(s) and/or goal(s) provided to him or her. And in a step916, steps 908 to 916 may be repeated such that the user or subject mayiteratively improve their cardiac health score and their overall health.

Although the above steps show method 900 of managing cardiac diseaseand/or rhythm in accordance with many embodiments, a person of ordinaryskill in the art will recognize many variations based on the teachingdescribed herein. The steps may be completed in a different order. Stepsmay be added or deleted. Some of the steps may comprise sub-steps. Manyof the steps may be repeated as often as beneficial to the user orsubject.

One or more of the steps of the method 900 may be performed withcircuitry, for example, one or more of a processor or a logic circuitrysuch as a programmable array logic for a field programmable gate array.The circuitry may be programmed to provide one or more of the steps ofthe method 900, and the program may comprise program instructions storedon a non-transitory computer readable medium or memory or programmedsteps of the logic circuitry such as the programmable array logic or thefield programmable gate array, for example.

In some embodiments, the heart rate information (or an extracted portionof HR information) may be used to compare to a database of similarinformation that has been correlated with cardiac events. For example,heart rate information may be compared to a database of HR informationextracted for ECG recordings of patients known to be experiencingcardiac problems. Thus, patterns of heart rate information taken from asubject may be compared to patterns of cardiac information in adatabase. If there is a match (or a match within a reasonable closenessof fit), the patient may be instructed to record an ECG, e.g. using anambulatory ECG monitor. This may then provide a more detailed view ofthe heart. This method may be particularly useful, as it may allowrecording and/or transmission and/or analysis of detailed electricalinformation about the heart at or near the time (or shortly thereafter)when a clinically significant cardiac event is occurring. Thus, thecontinuous monitoring may allow a subject to be alerted immediately uponan indication of the potential problem (e.g. an increase in HRVsuggestive of a cardiac dysfunction). This may allow the coupling ofcontinuous HR monitoring with ECG recording and analysis for diseasediagnosis and disease management.

FIG. 10 illustrates one variation of a method for monitoring a subjectto determine when to record an electrocardiogram (ECG). In FIG. 10, asubject is wearing a continuous heart rate monitor (configured as awatch 1010, including electrodes 1016), shown in step 1002. The heartrate monitor transmits (wirelessly 1012) heart rate information that isreceived by the smartphone 1018, as shown in step 1004. The smartphoneincludes a processor that may analyze the heart rate information 1004,and when an irregularity is determined, may indicate 1006 to the subjectthat an ECG should be recorded. In FIG. 10, an ambulatory ECG monitor1014 is attached (as a case having electrodes) to the phone 1018. Theuser may apply the ECG monitor as to their body (e.g. chest, betweenarms, etc.) 1008 to record ECGs that can then be saved and/ortransmitted for analysis.

FIGS. 11 and 11A show screenshots of an atrial fibrillation dashboard1100 of a user interface for the cardiac disease and/or rhythmmanagement system 100. FIG. 11 shows a top portion 1100 a of the atrialfibrillation dashboard 1100 while FIG. 10A shows a bottom portion 1100 bof the atrial fibrillation dashboard 1100.

The top portion 1100 a of the atrial fibrillation dashboard 1100 asshown in FIG. 10 may display the current cardiac health score of theuser or subject, a recent best cardiac health score of the user orsubject, and a completion percentage of recommendation(s) and/or goal(s)for the user or subject. The user or subject may tap any one of thecardiac health score displays or the recommendation(s) and/or goal(s)displays to access more detailed information regarding the calculatedhealth score(s) or recommendation(s) and/or goal(s), respectively. Thetop portion 1100 a may also show an ECG of the user or subject and abutton which may be tapped to record the ECG of the user or subject forthe day. As discussed with reference to FIG. 1, the ECG may be recordedwith a handheld sensor 103 in communication with the local computingdevice 100. The top portion 1000 a may also show the number of atrialfibrillation episodes and the average duration of these atrialfibrillation episodes. This number and duration may be generatedautomatically by software or logic of the mobile app 101 a based on orin response to the ECG measurements taken by the user or subject.Alternatively or in combination, a physician may access the atrialfibrillation dashboard 1100 of an individual user or subject, evaluatehis or her ECGs, and provide the number of atrial fibrillation episodesand their duration to the mobile app 101 a or other software loaded onthe local computing device 101 of the user or subject. The shortest andlongest durations of the atrial fibrillation episodes may also be shownby the top portion 1100 a as well as the user or subject's dailyadherence to a medication regime.

The bottom portion 1100 b of the atrial fibrillation dashboard 1100 asshown in FIG. 10A may display one or more influencers which influencehow the cardiac health score is generated. These influencers mayinclude, for example, caffeine intake, alcohol intake, stress levels,sleep levels, weight, nutrition, fitness and activity levels, and bloodpressure. Data for these influencers may be input automatically by oneor more biometric sensors coupled to the local computing device 101and/or the mobile app 101 a. Alternatively or in combination, the datafor these influencers may be input manually by the user or subject bytapping on the respective influencer display. For example, tapping onthe blood pressure display area may cause a slider input 1100 c forblood pressure to pop up. The user or subject may use the slider toenter and save his or her blood pressure for the day. Similar pop-ups oruser-selected inputs may be provided for the other influencers. Forexample, the user or subject may enter his or her daily caffeine oralcohol intake, stress and sleep levels, nutrition levels, or activityand fitness levels (e.g. low/bad, medium/so-so, or high/good based onthe user's age, gender, height, weight, etc. as can be indicated by aninstruction page of the mobile app 101 a). The influencer displays mayalso show the goal progression of the user or subject.

FIGS. 12 and 12A show screenshots of a goals and recommendations page1200 of the cardiac disease and rhythm management system interface ormobile app 101 a. A top portion 1200 a of the goals and recommendationspage 1100 may comprise a listing of 7-day goals for the user or subject.The top portion 1200 a may further comprise everyday goals for the useror subject which often cannot be removed or changed. The user or subjectcan check off these goals or recommendations as he or she meets them.The top portion 1200 a may track goal completion percentage over a 7-dayperiod. The user or subject can set the same goals for the next dayand/or set new goals.

A bottom portion 1200 b of the goals and recommendations page 1200 maycomprise a listing of new goals which the user or subject may add. Thenew goals may be categorized into goals or recommendations for atrialfibrillation management, stress management, and/or other categories. Forexample, goals for atrial fibrillation management may include takingdaily medications, reducing caffeine intake, and reducing alcoholintake. And, goals for stress management may include meditate for 5minutes daily, take blood pressure reading daily, and getting at least 7hours of sleep nightly. Using the goals and recommendations page 1200,the user or subject can set their goals for the week. One or more ofthese goals may be automatically recommended to the user or subject orbe recommended by a physician having access to the dashboard 1100. Forexample, goals may be recommended based on last week's progress. Thecompletion of recommended goals can result in the user or subjectearning more “points,” in effect gamifying health and cardiac rhythmmanagement for the user or subject. Alternatively or in combination, thegoals may be set by a physician having access to the dashboard 1100.

FIG. 13 shows a screenshot of a user's local computing device notifyingthe user with a pop-up notice 1300 to meet their daily recommendationsand goals. By tapping on the pop-up notice, 1300, the user or subjectcan be taken to the atrial fibrillation dashboard where the user orsubject can update or otherwise manage their cardiac health.

FIG. 14 shows an embodiment comprising a smart watch 1400 which includesat least one heart rate monitor 1402 and at least one activity monitor1404. One or more processors are coupled to one or more non-transitorymemories of the smart watch and configured to communicate with the heartrate monitor 1402 and the activity monitor 1404. The one or moreprocessors are further coupled to an output device 1408. Processorexecutable code is stored on the one or more memories and when executedby the one or more processors causes the one or more processors todetermine if heart rate and activity measurements represent an advisorycondition for recording an ECG, and generate and send notificationsignals through the output device 1408 when an advisory condition forrecording an ECG is determined.

For example, presently available smart watches include motion sensorssuch as pedometers. Pedometers can be based on an accelerometer orelectromechanical mechanism such as a pendulum, magnetic reed proximityswitch, and a spring suspended lever arm with metal-on-metal contact.Modern accelerometers are often small micro electro-mechanical systemsand are well known by those skilled in the art. Heart rate monitors arereadily available with smart phones as well as smart watches. One typeuses an optical sensor to detect the fluctuation of blood flow. Thesignal can be amplified further using, for example, a microcontroller tocount the rate of fluctuation, which is actually the heart rate.

An advisory condition for recording an ECG may occur due to, forexample, large continuing fluctuations in heart rate. An advisorycondition for recording an ECG can also occur when a measured heart rateincreases rapidly without a corresponding increase in activity monitoredby, for example, an accelerometer. By comparing measured heart ratechanges with measured activity changes, the presently disclosed softwareor “app” minimizes false alarms are minimized. ECG devices are describedin U.S. Ser. No. 12/796,188, filed Jun. 8, 2010, now U.S. Pat. No.8,509,882, hereby expressly incorporated herein by reference in itsentirety. The ECG device can be present in a smart watch band or a smartphone. In one embodiment, the ECG device includes an electrode assemblyconfigured to sense heart-related signals upon contact with a user'sskin, and to convert the sensed heart-related signals to an ECG electricsignal. The ECG device transmits an ultrasonic frequency modulated ECGsignal to a computing device such as, for example, a smartphone.Software running on the computing device or smartphone digitizes andprocesses the audio in real-time, where the frequency modulated ECGsignal is demodulated. The ECG can be further processed using algorithmsto calculate heart rate and identify arrhythmias. The ECG, heart rate,and rhythm information can be displayed on the computer or smartphone,stored locally for later retrieval, and/or transmitted in real-time to aweb server via a 2G/3G/4G, WiFi or other Internet connection. Inaddition to the display and local processing of the ECG data, thecomputer or smartphone can transmit, in real-time, the ECG, heart rateand rhythm data via a secure web connection for viewing, storage andfurther analysis via a web browser interface.

In another embodiment, the converter assembly of an ECG device isintegrated with, and electrically connected to the electrode assemblyand is configured to convert the electric ECG signal generated byelectrode assembly to a frequency modulated ECG ultrasonic signal havinga carrier frequency in the range of from about 18 kHz to about 24 kHz.It is sometimes desirable to utilize a carrier frequency in the 20 kHzto 24 kHz range. The ultrasonic range creates both a lower noise and asilent communication between the acquisition electronics and thecomputing device such as the smartphone, notebook, smart watch and thelike.

A kit can include downloadable software such as an “app” for detectingan advisory condition for recording an ECG and an ECG device. The ECGdevice can be present on a watch band for replacing a specific band on asmart watch. The ECG device can also be provided on a smart phone backplate for replacing an existing removable smartphone back. In anotherconfiguration, the ECG device is usable as a smartphone protective case.

Software on the smartphone or smart watch can also combine data andsignals from other sensors built into the smartphone or smart watch suchas a GPS.

While preferred embodiments of the present disclosure have been shownand described herein, it will be obvious to those skilled in the artthat such embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the subject matter described herein.It should be understood that various alternatives to the embodiments ofthe subject matter described herein may be employed in practicing thesubject matter described herein. It is intended that the followingclaims define the scope of the disclosure and that methods andstructures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A method of evaluating health of a heart of auser, the method comprising receiving heart rate information from aheart rate sensor located on a surface of a wearable computing deviceworn by a user; transmitting said heart rate information to a processorof said wearable computing device; determining an irregular heart ratevariability (HRV) value, with said processor, in response to saidreceived heart rate information; and sensing an electrocardiogram ofsaid user with said wearable computing device in response to saidirregular HRV value.
 2. The method of claim 1, comprising generating aHeart Health Score based on said determined HRV value and on or more ofsaid sensed electrocardiogram, a determined presence of an arrhythmia, anumber of premature beats, a frequency of said premature beats, and aHeart Rate Turbulence (HRT) value.
 3. The method of claim 2, furthercomprising displaying said Heart Health Score with a display of saidwearable computing device.
 4. The method of claim 1, wherein said heartrate information is measured by said heart rate sensor continuously forat least 2 hours.
 5. The method of claim 4, wherein said heart rateinformation is measured by said heart rate sensor continuously for atleast 7 days.
 6. The method of claim 2, wherein said Heart Health Scoreranges from a low of 1 to a high of
 100. 7. The method of claim 1,wherein said wearable computing device comprises a smartband or asmartwatch.
 8. The method of claim 2, wherein said heart health score istransmitted to a remote server or cloud server, and wherein said hearthealth score is accessible on said remote server or said cloud server toother users.
 9. The method of claim 1, wherein said step of sensing saidelectrocardiogram comprises providing an indication to said user tosense said electrocardiogram.
 10. The method of claim 9, wherein saidindication comprises an alert.
 11. The method of claim 1, comprisingproviding said user with an indication to input physiologic informationassociated with said received heart rate information into said wearablecomputing device.
 12. A method of determining a presence of anarrhythmia of a user, said method comprising receiving heart rateinformation from a heart rate sensor located on a surface of a wearablecomputing device worn by a user; transmitting said heart rateinformation to a processor of said wearable computing device;determining an heart rate variability (HRV) value, with said processor,in response to said received heart rate information; determining apresence of an atrial fibrillation of said user in response to saiddetermined heart rate variability (HRV) value; and sensing anelectrocardiogram with said wearable computing device in response tosaid presence of said atrial fibrillation.
 13. The method of claim 12,further comprising displaying to said user an alert on said display ofsaid wearable device if said presence of said atrial fibrillation isdetermined.
 14. The method of claim 12, comprising receiving heart rateinformation from a plurality of heart rate sensors coupled to aplurality of users.
 15. The method of claim 14, further comprisingtraining a machine learning algorithm to recognize a presence of anatrial fibrillation of an individual user of the plurality of users inresponse to said heart rate information from the plurality of heart ratesensors.
 16. The method of claim 15, wherein said plurality of usershave been previously identified as having atrial fibrillation or ashaving no atrial fibrillation.
 17. The method of claim 15, whereindetermining said presence of said atrial fibrillation of said user inresponse to said determined heart rate (HRV) value comprises determiningsaid presence of said atrial fibrillation of said user using saidtrained machine learning algorithm.
 18. The method of claim 12,comprising receiving activity level data associated with said receivedheart rate information from an activity level sensor on said wearablecomputing device.
 19. The method of claim 12, wherein said presence ofsaid atrial fibrillation is determined in part by comparing saiddetermined HRV value with said received activity level.
 20. The methodof claim 19, wherein said step of sensing said electrocardiogramcomprises providing an indication to said user to sense saidelectrocardiogram.
 21. The method of claim 20, wherein said indicationcomprises an alert.